This contribution is from david corliss david teaches a class on this subject, giving a (very brief) description of 23 regression methods in just an hour, wit.
Introduction to regression and data analysis with dan campbell and sherlock campbell a types of variables your variables may take several forms, and it will be important later that you are aware of, and understand, the nature of your variables the following variables are those which you are most likely to encounter in your research.
Regression analysis with count dependent variables if your dependent variable is a count of items, events, results, or activities, you might need to use a different type of regression model counts are nonnegative integers (0, 1, 2, etc) count data with higher means tend to be normally distributed and you can often use ols. Regression techniques are one of the most popular statistical techniques used for predictive modeling and data mining tasks on average, analytics professionals know only 2-3 types of regression which are commonly used in real world they are linear and logistic regression.
Ridge regression is a remedial measure taken to alleviate collinearity amongst regression predictor variables in a model collinearity is a phenomenon in which one feature variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. Jackknife regression: this is the new type of regression, also used as general clustering and data reduction technique it solves all the drawbacks of traditional regression it solves all the drawbacks of traditional regression.
Within multiple types of regression models, it is important to choose the best suited technique based on type of independent and dependent variables, dimensionality in the data and other essential characteristics of the data.